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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A) Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
B) Ensuring the ETL pipeline uses only batch processing for data ingestion.
C) Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
D) Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
E) Using a single storage location for both raw and transformed data to simplify the workflow.

2. You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A) Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
B) Store the entire dataset in a single large file to minimize the number of files in the directory.
C) Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
D) Split the dataset into multiple files based on a logical partitioning key, such as a date column.

3. You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A) Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.
B) Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.
C) Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
D) Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.

4. You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A) Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
B) Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
C) Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
D) Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.

5. A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A) Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
B) Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
C) Perform all analysis on the CPU to avoid potential GPU memory limitations.
D) Downsample the dataset and analyze a subset using Pandas for efficiency.

Solutions: